Scoring predictive models using a reduced representation of proteins: Model and energy definition

Federico Fogolari, Lidia Pieri, Agostino Dovier, Luca Bortolussi, Gilberto Giugliarelli, Alessandra Corazza, Gennaro Esposito, Paolo Viglino

Research output: Contribution to journalArticle

Abstract

Background. Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment. Results. All protein structures in the set top500H have been converted in reduced form. The distribution of pseudobonds, pseudoangle, pseudodihedrals and distances between centers of interactions have been converted into potentials of mean force. A suitable reference distribution has been defined for non-bonded interactions which takes into account excluded volume effects and protein finite size. The correlation between adjacent main chain pseudodihedrals has been converted in an additional energetic term which is able to account for cooperative effects in secondary structure elements. Local energy surface exploration is performed in order to increase the robustness of the energy function. Conclusion. The model and the energy definition proposed have been tested on all the multiple decoys' sets in the Decoys'R'us database. The energetic model is able to recognize, for almost all sets, native-like structures (RMSD less than 2.0 Å). These results and those obtained in the blind CASP7 quality assessment experiment suggest that the model compares well with scoring potentials with finer granularity and could be useful for fast exploration of conformational space. Parameters are available at the url: http://www.dstb.uniud.it/∼ffogolari/download/.

Original languageEnglish (US)
Article number15
JournalBMC Structural Biology
Volume7
DOIs
StatePublished - Apr 27 2007

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Proteins
Space Flight
Structural Models
Protein Folding
Computational Biology
Databases
Amino Acids

ASJC Scopus subject areas

  • Structural Biology

Cite this

Fogolari, F., Pieri, L., Dovier, A., Bortolussi, L., Giugliarelli, G., Corazza, A., ... Viglino, P. (2007). Scoring predictive models using a reduced representation of proteins: Model and energy definition. BMC Structural Biology, 7, [15]. https://doi.org/10.1186/1472-6807-7-15

Scoring predictive models using a reduced representation of proteins : Model and energy definition. / Fogolari, Federico; Pieri, Lidia; Dovier, Agostino; Bortolussi, Luca; Giugliarelli, Gilberto; Corazza, Alessandra; Esposito, Gennaro; Viglino, Paolo.

In: BMC Structural Biology, Vol. 7, 15, 27.04.2007.

Research output: Contribution to journalArticle

Fogolari, F, Pieri, L, Dovier, A, Bortolussi, L, Giugliarelli, G, Corazza, A, Esposito, G & Viglino, P 2007, 'Scoring predictive models using a reduced representation of proteins: Model and energy definition', BMC Structural Biology, vol. 7, 15. https://doi.org/10.1186/1472-6807-7-15
Fogolari, Federico ; Pieri, Lidia ; Dovier, Agostino ; Bortolussi, Luca ; Giugliarelli, Gilberto ; Corazza, Alessandra ; Esposito, Gennaro ; Viglino, Paolo. / Scoring predictive models using a reduced representation of proteins : Model and energy definition. In: BMC Structural Biology. 2007 ; Vol. 7.
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